Python 2.7.16 |Anaconda, Inc.| (default, Sep 24 2019, 16:55:38)

Type "copyright", "credits" or "license" for more information.

IPython 5.8.0 -- An enhanced Interactive Python.

? -> Introduction and overview of IPython's features.

%quickref -> Quick reference.

help -> Python's own help system.

object? -> Details about 'object', use 'object??' for extra details.

In [1]: runfile('/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py', wdir='/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts')

435

266

task_manager

initial_guess

task_manager

initial_guess

run_mcmc

_initialize_chains

_step

_get_Rhat

_get_psi

INITIAL GUESSES 0.8603618557948427 0.0 1.0150310484183036 0.0 0.43680641718574476 1.0 0.229166666667 -0.4609759673362414 3.3848112460971858

INITIAL GUESSES 0.5866818546597699 0.0 0.7323360903664308 0.0 0.3996339435829777 1.0 0.228070175439 -0.2993867113994893 3.2168053947906308

step

INITIAL GUESSES 0.6034983548093563 0.0 0.6983298627011384 0.0 0.3754780741090389 1.0 0.286343612335 0.22075428097912525 2.421174237327158

INITIAL GUESSES 0.9247275629974878 0.0 1.0492959614153001 0.0 0.42250871361282566 1.0 0.175213675214 -0.4060474872456063 3.8679457839628113

step

zeta acceptanceProb 0 0 185 -995.3256338877243 5.405762959381243 0.0

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:597: RuntimeWarning: divide by zero encountered in log10

scatter1 = ax1.scatter(self.xi[idx_z_bin][idx_sort], self.eta[idx_z_bin][idx_sort], c=np.log10((z_good/z_bad)[idx_sort]), cmap=mpl.cm.get_cmap('coolwarm'), alpha=1.0, vmin=-2.5, vmax=2.5, s=100)

/opt/anaconda2/lib/python2.7/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.

warnings.warn(message, mplDeprecation, stacklevel=1)

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:646: RuntimeWarning: divide by zero encountered in log10

scatter2 = ax2.scatter(self.xi[idx_z_bin][idx_sort], self.eta[idx_z_bin][idx_sort], c=np.log10((z_good/z_bad)[idx_sort]), cmap=mpl.cm.get_cmap('coolwarm'), alpha=1.0, vmin=-2.5, vmax=2.5, s=100)

Traceback (most recent call last):

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/formatters.py", line 334, in __call__

return printer(obj)

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 247, in <lambda>

png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 131, in print_figure

fig.canvas.print_figure(bytes_io, **kw)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backend_bases.py", line 2212, in print_figure

**kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 517, in print_png

FigureCanvasAgg.draw(self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 437, in draw

self.figure.draw(self.renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/figure.py", line 1493, in draw

renderer, self, artists, self.suppressComposite)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.py", line 2635, in draw

mimage._draw_list_compositing_images(renderer, self, artists)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/legend.py", line 775, in draw

bbox = self._legend_box.get_window_extent(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 266, in get_window_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 831, in get_extent

bbox, info, d = self._text._get_layout(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/text.py", line 309, in _get_layout

ismath=ismath)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 236, in get_text_width_height_descent

s, fontsize, renderer=self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 501, in get_text_width_height_descent

dvifile = self.make_dvi(tex, fontsize)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 362, in make_dvi

with Locked(self.texcache):

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/cbook/__init__.py", line 2529, in __enter__

raise self.TimeoutError(err_str)

TimeoutError: LOCKERROR: matplotlib is trying to acquire the lock

'/Users/lester/.matplotlib/tex.cache/.matplotlib_lock-*'

and has failed. This maybe due to any other process holding this

lock. If you are sure no other matplotlib process is running try

removing these folders and trying again.

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:1186: RuntimeWarning: divide by zero encountered in log

prob_arr[j] = np.log(self.piBeagle[i,j])+multivariate_normal.logpdf(values,mean,cov)

zeta acceptanceProb 0 3 185 -1447.2842725865985 4.818998735219411 0.0

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:1186: RuntimeWarning: divide by zero encountered in log

prob_arr[j] = np.log(self.piBeagle[i,j])+multivariate_normal.logpdf(values,mean,cov)

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:1186: RuntimeWarning: divide by zero encountered in log

prob_arr[j] = np.log(self.piBeagle[i,j])+multivariate_normal.logpdf(values,mean,cov)

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:1186: RuntimeWarning: divide by zero encountered in log

prob_arr[j] = np.log(self.piBeagle[i,j])+multivariate_normal.logpdf(values,mean,cov)

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500

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500

scatter1 = ax1.scatter(self.xi[idx_z_bin][idx_sort], self.eta[idx_z_bin][idx_sort], c=np.log10((z_good/z_bad)[idx_sort]), cmap=mpl.cm.get_cmap('coolwarm'), alpha=1.0, vmin=-2.5, vmax=2.5, s=100)

500

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:646: RuntimeWarning: divide by zero encountered in log10

scatter2 = ax2.scatter(self.xi[idx_z_bin][idx_sort], self.eta[idx_z_bin][idx_sort], c=np.log10((z_good/z_bad)[idx_sort]), cmap=mpl.cm.get_cmap('coolwarm'), alpha=1.0, vmin=-2.5, vmax=2.5, s=100)

500

/opt/anaconda2/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.py:1745: RuntimeWarning: divide by zero encountered in double_scalars

x = np.asarray((x - loc)/scale, dtype=dtyp)

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/opt/anaconda2/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.py:1745: RuntimeWarning: divide by zero encountered in double_scalars

x = np.asarray((x - loc)/scale, dtype=dtyp)

/opt/anaconda2/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.py:1745: RuntimeWarning: divide by zero encountered in double_scalars

x = np.asarray((x - loc)/scale, dtype=dtyp)

/opt/anaconda2/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.py:1745: RuntimeWarning: divide by zero encountered in double_scalars

x = np.asarray((x - loc)/scale, dtype=dtyp)

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zeta acceptanceProb 0 545 185 -972.502381947574 4.956167639523686 0.0

zeta acceptanceProb 0 554 185 -2381.535548858051 3.7538317900110236 0.0

zeta acceptanceProb 0 549 185 -1222.675115012245 4.546383350111026 0.0

zeta acceptanceProb 0 553 185 -4407.543823654431 5.591621659460614 0.0

zeta acceptanceProb 0 554 185 -2170.8791477300106 5.591292109717065 0.0

zeta acceptanceProb 0 555 185 -1125.692245350196 3.0060917485376897 0.0

zeta acceptanceProb 0 560 185 -1009.0137073928252 4.116904295870306 0.0

zeta acceptanceProb 0 565 185 -875.2289751800902 3.542451053853663 0.0

zeta acceptanceProb 0 562 185 -1758.7809742723246 3.2884308460681053 0.0

zeta acceptanceProb 0 564 32 -1033.781839912879 6.6043648827022725 0.0

zeta acceptanceProb 0 564 185 -817.1335849069678 3.9192004758362318 0.0

zeta acceptanceProb 0 565 32 -1446.4341799965387 4.54755903833594 0.0

zeta acceptanceProb 0 571 185 -1075.9481639700616 5.081848295289081 0.0

zeta acceptanceProb 0 566 32 -1040.9496285022512 5.101181726132674 0.0

zeta acceptanceProb 0 567 32 -1058.0674529288087 3.5640027483443815 0.0

zeta acceptanceProb 0 572 185 -2572.362289683205 5.081848295289081 0.0

zeta acceptanceProb 0 568 32 -972.4288509602263 4.631801283774975 0.0

zeta acceptanceProb 0 568 185 -845.7032748989291 4.776075952579183 0.0

zeta acceptanceProb 0 575 185 -1627.1473113201487 4.289469779502392 0.0

zeta acceptanceProb 0 576 185 -1318.4228918466304 4.014449532045811 0.0

zeta acceptanceProb 0 578 185 -1228.799862190359 4.016881921181193 0.0

zeta acceptanceProb 0 573 185 -1116.5588156100428 0.5192578793388618 0.0

zeta acceptanceProb 0 579 185 -1335.7092046196335 4.486681872245142 0.0

zeta acceptanceProb 0 575 185 -1190.6558208617455 4.0632494873302605 0.0

zeta acceptanceProb 0 576 32 -990.4867774677617 5.380885992239997 0.0

zeta acceptanceProb 0 577 32 -1376.333698493589 3.989149596858258 0.0

zeta acceptanceProb 0 584 185 -2134.0585131231323 2.82091069592076 0.0

zeta acceptanceProb 0 583 185 -1650.5364429538954 4.667791423953371 0.0

zeta acceptanceProb 0 584 32 -1140.9238284319767 5.794333728660084 0.0

zeta acceptanceProb 0 585 185 -1768.5026890681 4.727029231171377 0.0

zeta acceptanceProb 0 586 32 -964.6333804066147 5.657668965044066 0.0

zeta acceptanceProb 0 587 185 -1215.1368485446246 4.726712922608742 0.0

zeta acceptanceProb 0 591 185 -1138.0260940733842 4.7197523956082925 0.0

zeta acceptanceProb 0 593 32 -1276.7571755876234 5.037957756746609 0.0

zeta acceptanceProb 0 593 185 -958.3443449282059 1.4571520170890953 0.0

zeta acceptanceProb 0 594 32 -3943.089500019186 4.901996877248503 0.0

zeta acceptanceProb 0 595 185 -756.5845977725154 3.334017919630025 0.0

zeta acceptanceProb 0 597 32 -953.3262309618723 4.910910755130021 0.0

zeta acceptanceProb 0 598 32 -1352.7780516506064 4.942807825906475 0.0

zeta acceptanceProb 0 599 32 -747.5893396977876 5.257056738302168 0.0

zeta acceptanceProb 0 600 32 -766.4680641968115 4.097931002710298 0.0

zeta acceptanceProb 0 602 185 -883.3668055438206 -1.4849966271041612 0.0

zeta acceptanceProb 0 608 185 -785.7949956925398 2.5971026980787526 0.0

zeta acceptanceProb 0 605 32 -1577.310930240199 4.257504802146211 0.0

zeta acceptanceProb 0 609 32 -794.5121448222135 6.186279546447723 0.0

zeta acceptanceProb 0 612 185 -763.7441444925269 0.4572168129535221 0.0

zeta acceptanceProb 0 613 185 -972.5372505673745 4.2070343136771085 0.0

zeta acceptanceProb 0 624 32 -847.1576451300043 6.4127903071546015 0.0

zeta acceptanceProb 0 624 185 -797.0437880219622 2.063616017168508 0.0

zeta acceptanceProb 0 629 185 -826.4523619175824 2.4106982422197927 0.0

zeta acceptanceProb 0 630 32 -1528.842489262027 6.280745425402779 0.0

zeta acceptanceProb 0 639 185 -784.5973404294735 3.353002918711302 0.0

zeta acceptanceProb 0 641 185 -1726.3998194431324 2.6028064391128547 0.0

zeta acceptanceProb 0 646 185 -1476.7214179774483 2.4347867476284595 0.0

zeta acceptanceProb 0 665 185 -922.2188725856124 4.6437429149918925 0.0

zeta acceptanceProb 0 662 185 -899.4815829814555 1.4193892154302619 0.0

zeta acceptanceProb 0 672 185 -955.0046877184006 3.172122194740612 0.0

zeta acceptanceProb 0 678 185 -1066.882883092738 4.214916299860682 0.0

zeta acceptanceProb 0 680 185 -1012.3288355246384 4.69643655502502 0.0

zeta acceptanceProb 0 683 185 -1923.7203138310122 3.6026035209035085 0.0

zeta acceptanceProb 0 682 32 -1636.9816648313138 6.597847685651999 0.0

zeta acceptanceProb 0 683 185 -1351.0204599651306 1.646583046350833 0.0

zeta acceptanceProb 0 688 185 -943.5639673406888 2.252048730346122 0.0

zeta acceptanceProb 0 689 185 -774.7826228232929 0.5851155653312423 0.0

zeta acceptanceProb 0 692 185 -1060.0020060529928 2.2822584886633126 0.0

zeta acceptanceProb 0 696 185 -824.2948961193496 4.232626853671194 0.0

zeta acceptanceProb 0 697 185 -886.3043106328893 3.699803222769506 0.0

zeta acceptanceProb 0 699 185 -1902.849169273489 4.446963423998676 0.0

zeta acceptanceProb 0 700 32 -1440.0333102855934 5.528400400855199 0.0

zeta acceptanceProb 0 712 185 -895.1424238511717 3.937278546844943 0.0

zeta acceptanceProb 0 760 32 -786.0173509559734 5.851089091121496 0.0

zeta acceptanceProb 0 764 32 -778.0430995559839 6.103070983638454 0.0

zeta acceptanceProb 0 779 185 -1198.9874091927693 5.033053685077258 0.0

zeta acceptanceProb 0 793 185 -934.6501239652454 3.0126417064556463 0.0

zeta acceptanceProb 0 810 32 -1561.1583823791514 6.021369985191816 0.0

zeta acceptanceProb 0 818 185 -758.631258510848 4.255669420838839 0.0

zeta acceptanceProb 0 843 185 -1017.4833223710637 0.18148916559715822 0.0

zeta acceptanceProb 0 844 32 -1279.682017347815 5.950871506851275 0.0

zeta acceptanceProb 0 878 32 -974.6799790918373 6.54716109850518 0.0

zeta acceptanceProb 0 886 32 -931.7096391568017 4.286612360803592 0.0

eta acceptanceProb 0 930 260 -1435.2127573138803 2.2697733772901802 0.0

1000

Traceback (most recent call last):

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/formatters.py", line 334, in __call__

return printer(obj)

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 247, in <lambda>

png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 131, in print_figure

fig.canvas.print_figure(bytes_io, **kw)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backend_bases.py", line 2212, in print_figure

**kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 517, in print_png

FigureCanvasAgg.draw(self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 437, in draw

self.figure.draw(self.renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/figure.py", line 1493, in draw

renderer, self, artists, self.suppressComposite)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.py", line 2635, in draw

mimage._draw_list_compositing_images(renderer, self, artists)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/legend.py", line 775, in draw

bbox = self._legend_box.get_window_extent(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 266, in get_window_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 831, in get_extent

bbox, info, d = self._text._get_layout(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/text.py", line 309, in _get_layout

ismath=ismath)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 236, in get_text_width_height_descent

s, fontsize, renderer=self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 501, in get_text_width_height_descent

dvifile = self.make_dvi(tex, fontsize)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 362, in make_dvi

with Locked(self.texcache):

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/cbook/__init__.py", line 2529, in __enter__

raise self.TimeoutError(err_str)

TimeoutError: LOCKERROR: matplotlib is trying to acquire the lock

'/Users/lester/.matplotlib/tex.cache/.matplotlib_lock-*'

and has failed. This maybe due to any other process holding this

lock. If you are sure no other matplotlib process is running try

removing these folders and trying again.

Traceback (most recent call last):

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/formatters.py", line 334, in __call__

return printer(obj)

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 247, in <lambda>

png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 131, in print_figure

fig.canvas.print_figure(bytes_io, **kw)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backend_bases.py", line 2212, in print_figure

**kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 517, in print_png

FigureCanvasAgg.draw(self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 437, in draw

self.figure.draw(self.renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/figure.py", line 1493, in draw

renderer, self, artists, self.suppressComposite)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.py", line 2635, in draw

mimage._draw_list_compositing_images(renderer, self, artists)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/legend.py", line 775, in draw

bbox = self._legend_box.get_window_extent(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 266, in get_window_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 831, in get_extent

bbox, info, d = self._text._get_layout(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/text.py", line 309, in _get_layout

ismath=ismath)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 236, in get_text_width_height_descent

s, fontsize, renderer=self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 501, in get_text_width_height_descent

dvifile = self.make_dvi(tex, fontsize)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 362, in make_dvi

with Locked(self.texcache):

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/cbook/__init__.py", line 2529, in __enter__

raise self.TimeoutError(err_str)

TimeoutError: LOCKERROR: matplotlib is trying to acquire the lock

'/Users/lester/.matplotlib/tex.cache/.matplotlib_lock-*'

and has failed. This maybe due to any other process holding this

lock. If you are sure no other matplotlib process is running try

removing these folders and trying again.

zeta acceptanceProb 0 1032 32 -1489.8976544289017 6.497278115037871 0.0

zeta acceptanceProb 0 1067 32 -1247.7946063912045 6.091520108039147 0.0

zeta acceptanceProb 0 1064 61 -761.6187619381487 4.608963869615923 0.0

zeta acceptanceProb 0 1187 61 -815.0766480381847 4.858873474866984 0.0

zeta acceptanceProb 0 1391 234 -961.1512937589661 4.813640034147037 0.0

1500 15000

1500

1500 15000

1500

1500 15000

1500

Traceback (most recent call last):

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/formatters.py", line 334, in __call__

return printer(obj)

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 247, in <lambda>

png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 131, in print_figure

fig.canvas.print_figure(bytes_io, **kw)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backend_bases.py", line 2212, in print_figure

**kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 517, in print_png

FigureCanvasAgg.draw(self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 437, in draw

self.figure.draw(self.renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/figure.py", line 1493, in draw

renderer, self, artists, self.suppressComposite)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.py", line 2635, in draw

mimage._draw_list_compositing_images(renderer, self, artists)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/legend.py", line 775, in draw

bbox = self._legend_box.get_window_extent(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 266, in get_window_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 831, in get_extent

bbox, info, d = self._text._get_layout(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/text.py", line 309, in _get_layout

ismath=ismath)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 236, in get_text_width_height_descent

s, fontsize, renderer=self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 501, in get_text_width_height_descent

dvifile = self.make_dvi(tex, fontsize)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 362, in make_dvi

with Locked(self.texcache):

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/cbook/__init__.py", line 2529, in __enter__

raise self.TimeoutError(err_str)

TimeoutError: LOCKERROR: matplotlib is trying to acquire the lock

'/Users/lester/.matplotlib/tex.cache/.matplotlib_lock-*'

and has failed. This maybe due to any other process holding this

lock. If you are sure no other matplotlib process is running try

removing these folders and trying again.

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.794594798871781 0.0 0.0 1.5777214931061376 0.0 0.0 0.460642522872 1.0 0.500851625086 0.26598040842 0.59278163323 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.5696166942790002 0.0 0.0 1.4493933228458638 0.0 0.0 0.590020665671 1.0 0.504145892628 0.224777403797 0.340647914907 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.52495411129563 0.0 0.0 1.3106196125190275 0.0 0.0 0.402814098154 1.0 0.510667419452 0.469696718781 0.562391540526 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

xi acceptanceProb 0 1670 73 -4130.288308783125 0.6687159905319516 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.5866557818967606 0.0 0.0 1.4121894452689547 0.0 0.0 0.477448087025 1.0 0.543062891989 0.387605193255 0.510014908636 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.6948950952550892 0.0 0.0 1.535616856215451 0.0 0.0 0.461158461364 1.0 0.56125929322 0.387605193255 0.510014908636 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.6948950952550892 0.0 0.0 1.535616856215451 0.0 0.0 0.461158461364 1.0 0.520184944088 0.428127223892 0.403912963307 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.7898797352021332 0.0 0.0 1.656541478328576 0.0 0.0 0.400544849076 1.0 0.51083844761 0.509310589398 0.516983575128 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.7898797352021332 0.0 0.0 1.656541478328576 0.0 0.0 0.383584547139 1.0 0.510171477143 0.509310589398 0.516983575128 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.7898797352021332 0.0 0.0 1.656541478328576 0.0 0.0 0.383584547139 1.0 0.507213899036 0.417775877054 0.516983575128 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.9174950867969935 0.0 0.0 1.7138556321914051 0.0 0.0 0.383584547139 1.0 0.536859512658 0.28768451662 0.516983575128 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 2.045742444966012 0.0 0.0 1.9335976971341002 0.0 0.0 0.410169312147 1.0 0.51165004908 0.519035809734 0.485690818232 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

2000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.8358124037509702 0.0 0.0 1.8105494005448943 0.0 0.0 0.37152636559 1.0 0.508598704461 0.519035809734 0.485690818232 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

2000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7003106017917778 0.0 0.0 0.8129557858763222 0.0 0.0 0.168236524126 1.0 0.512357721976 0.885158846678 0.799133323229 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7329297605948479 0.0 0.0 0.8324102467463915 0.0 0.0 0.109532781379 1.0 0.555938956997 0.90312904132 0.746232023896 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6570268999270948 0.0 0.0 0.7988500174789982 0.0 0.0 0.153405369639 1.0 0.503701370796 0.833937413878 0.875660857088 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.380140361202328 0.0 0.0 1.1897058480195748 0.0 0.0 0.605195238937 1.0 0.516315478882 0.12368373621 0.457020269335 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 2155 172 -834.7357741936838 -4.716049350985279 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8111130429271733 0.0 0.0 0.8775228518631822 0.0 0.0 0.210236800624 1.0 0.631403660417 0.788066439545 0.911187911686 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 2209 172 -2094.66021880612 -1.6000460912020082 0.0

zeta acceptanceProb 0 2218 172 -963.2778512666496 -0.887145119724098 0.0

eta acceptanceProb 0 2246 95 -1082.2752166141447 2.585157560078272 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7883983626660187 0.0 0.0 0.8236427067995289 0.0 0.0 0.180765684845 1.0 0.551540816084 0.866161302902 0.79678953037 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7156878534493253 0.0 0.0 0.7510617788356095 0.0 0.0 0.142851082876 1.0 0.513070269231 0.882839673659 0.940056483926 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9530131874597386 0.0 0.0 0.906296775153212 0.0 0.0 0.28987229835 1.0 0.510312591038 0.722894602923 1.14067786431 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 2358 72 -757.0918582945492 3.647215039350252 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0106488972837335 0.0 0.0 1.065317958128593 0.0 0.0 0.235590487204 1.0 0.529615321476 0.602614007504 0.793476966338 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 2474 172 -799.8069077921383 0.3430323745029114 0.0

zeta acceptanceProb 0 2479 172 -902.372526395665 0.2786788213425914 0.0

zeta acceptanceProb 0 2487 172 -874.8307015042591 -2.444772032082322 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.709053821352737 0.0 0.0 0.8068917436493931 0.0 0.0 0.104512716338 1.0 0.553001109587 0.944497358051 0.906411064613 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 2489 172 -1031.4562281603828 -1.3091966480389932 0.0

zeta acceptanceProb 0 2491 172 -1304.5724160617217 -1.1225249606165284 0.0

2500

zeta acceptanceProb 0 2497 172 -826.8454945327326 -0.9390742488514787 0.0

2500

zeta acceptanceProb 0 2506 172 -787.5371044460389 -0.6339510720359782 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0529874946447442 0.0 0.0 1.0473582590825594 0.0 0.0 0.311098806518 1.0 0.532185819991 1.1376994589 0.734549318531 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 2564 172 -929.0034341857037 -4.555917300092382 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7212222017332749 0.0 0.0 0.7478777466563784 0.0 0.0 0.174657556857 1.0 0.525088915498 1.52504173155 0.866619905655 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

xi acceptanceProb 0 2569 211 -2064.170185865909 -1.01757073719298 0.0

zeta acceptanceProb 0 2569 211 -5742.944768730726 3.492797062983407 0.0

zeta acceptanceProb 0 2570 211 -24505.235000673187 3.045372461965569 0.0

zeta acceptanceProb 0 2573 172 -777.8408964995125 0.2536507334650428 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7713843234436414 0.0 0.0 0.7675209635603422 0.0 0.0 0.167678470297 1.0 0.509430307988 0.9546717815 0.813891035295 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 2612 172 -974.3458586314487 -1.1837402236606984 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1650201294962546 0.0 0.0 1.1414925611436937 0.0 0.0 0.318223460941 1.0 0.570316315547 1.21312625964 0.900991013455 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 2638 172 -1066.3112631886524 -0.2086163901312128 0.0

zeta acceptanceProb 0 2668 172 -863.5047603308831 -0.4184419831909113 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0702196589894513 0.0 0.0 0.9532739313270439 0.0 0.0 0.371231588591 1.0 0.512955448042 0.926658990008 0.970234534062 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

3000 15000

3000

3000 15000

3000

3000 15000

3000

3000 15000

3000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.136201267183638 0.0 0.0 1.1107604678654883 0.0 0.0 0.275580341429 1.0 0.56393636448 1.10301628281 0.882966990766 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9131109769480203 0.0 0.0 0.9688604705483049 0.0 0.0 0.192825136242 1.0 0.567772959969 0.727933253588 0.789138127757 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9065183188330459 0.0 0.0 0.9841631501766906 0.0 0.0 0.190968019357 1.0 0.541068518385 0.867229386035 0.904488728279 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8671478713757834 0.0 0.0 0.9472348467953675 0.0 0.0 0.210056384817 1.0 0.525093315494 0.777581490693 0.830536274185 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9205596428372445 0.0 0.0 0.9751477387001919 0.0 0.0 0.220511653529 1.0 0.530026129125 0.68567434373 0.85280199528 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9205596428372445 0.0 0.0 0.9751477387001919 0.0 0.0 0.221720329367 1.0 0.615787683871 0.68567434373 0.85280199528 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 3153 260 -1022.3225446450662 -0.02040818252172638 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8531271701245591 0.0 0.0 0.9072823956029079 0.0 0.0 0.199043075014 1.0 0.723910655436 0.893226364255 0.873480341554 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9487241913375052 0.0 0.0 1.0060840776817548 0.0 0.0 0.200300325895 1.0 0.533240659335 1.10606087892 0.908856952016 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 3175 72 -756.3403530909422 -0.23289973768309924 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0088787791938396 0.0 0.0 1.0294828249391281 0.0 0.0 0.239877338338 1.0 0.549273481579 0.695485437791 0.997850292127 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

xi acceptanceProb 0 3200 73 -28054.14000736551 2.2470011009160538 0.0

zeta acceptanceProb 0 3200 73 -1300.4849085983783 3.3805932282979256 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.015727705098762 0.0 0.0 1.0204962489939704 0.0 0.0 0.244893901991 1.0 0.532036121702 0.787727674517 0.832241712447 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1525978551808203 0.0 0.0 1.2039834764633204 0.0 0.0 0.246818421505 1.0 0.564794788076 0.71558812301 0.89892709442 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1926589173214823 0.0 0.0 1.2361760595182065 0.0 0.0 0.243696615174 1.0 0.543834325999 0.71558812301 0.846978129525 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1748357369502114 0.0 0.0 1.2041982461864822 0.0 0.0 0.232220069574 1.0 0.538935473664 1.06144182595 0.768839854978 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0856831261775797 0.0 0.0 1.1116262875214797 0.0 0.0 0.320224602145 1.0 0.540365621741 0.84962922413 0.986654141022 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

3500

xi acceptanceProb 0 3539 73 -63681.50542975427 3.0832277862064124 0.0

zeta acceptanceProb 0 3539 73 -749.817452172107 3.172257826491071 0.0

zeta acceptanceProb 0 3658 211 -3033.7144334644595 3.269892293665933 0.0

xi acceptanceProb 0 3659 211 -1570.4574613004154 2.998340499040532 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0862055726164717 0.0 0.0 1.0705533998030887 0.0 0.0 0.309991069507 1.0 0.540315611224 0.962442942551 0.767338381811 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0277972428923776 0.0 0.0 0.9357431022055842 0.0 0.0 0.301433217338 1.0 0.537713655073 0.763339626028 0.946003226167 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2022904961087253 0.0 0.0 1.1945435909588173 0.0 0.0 0.271348792011 1.0 0.556100961654 1.0438268692 0.88672738914 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1354194901707169 0.0 0.0 1.1377442480507212 0.0 0.0 0.250691253724 1.0 0.519768686015 0.879148618926 0.882990348428 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2248098376800824 0.0 0.0 1.242859604788664 0.0 0.0 0.226495932271 1.0 0.520081324558 0.896509325056 0.782479776715 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.159213997591466 0.0 0.0 1.1407294278507414 0.0 0.0 0.245193212366 1.0 0.510795986391 0.792659367289 0.819907050263 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1954512938704853 0.0 0.0 1.157291892724901 0.0 0.0 0.223623752732 1.0 0.513199865092 0.833596212374 0.899677790141 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0737100782063624 0.0 0.0 0.9437515630865199 0.0 0.0 0.323586256435 1.0 0.587490389248 1.14018570245 0.939038489139 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0913359067523667 0.0 0.0 0.9672467228381636 0.0 0.0 0.306452238903 1.0 0.505047020473 0.965159842356 0.968927711846 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9116393842463392 0.0 0.0 0.9941437674385097 0.0 0.0 0.195829059579 1.0 0.539059859849 0.784506142536 0.711663169157 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

4000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1428792672692165 0.0 0.0 1.058354742561847 0.0 0.0 0.282615105098 1.0 0.527676736656 0.807593032747 0.871735453038 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.172960105140604 0.0 0.0 1.161077091608122 0.0 0.0 0.319755824645 1.0 0.506113643679 1.08347247389 0.864188753506 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

xi acceptanceProb 0 4097 73 -59574.990055137634 3.5201141975900803 0.0

zeta acceptanceProb 0 4097 73 -8687.141368624738 3.4690657973400922 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9281061172469636 0.0 0.0 0.899716227758325 0.0 0.0 0.279233661904 1.0 0.574568044938 0.836490991584 1.06049027198 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8709467204946167 0.0 0.0 0.9279335658952805 0.0 0.0 0.234071169655 1.0 0.549055883125 0.967697996552 0.954703877996 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0223834712072644 0.0 0.0 1.0493679960645512 0.0 0.0 0.19937989742 1.0 0.548363833507 0.849328125137 0.925063486594 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0175151931820654 0.0 0.0 1.027275374635377 0.0 0.0 0.2824289186 1.0 0.574187484338 1.08598864265 0.83759291129 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9909304756751599 0.0 0.0 1.0034300503911913 0.0 0.0 0.279896091335 1.0 0.59851667568 0.743216923597 0.827503101904 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8376323208341505 0.0 0.0 0.8447199892357931 0.0 0.0 0.181201553053 1.0 0.518439812468 0.70911297334 0.790239447307 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7612520566876436 0.0 0.0 0.7806361685262625 0.0 0.0 0.16916861021 1.0 0.501685781598 0.807583637938 1.0245390161 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0465717506706476 0.0 0.0 1.0518264226875824 0.0 0.0 0.256938991376 1.0 0.510820788468 1.08839727391 0.994078943311 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1312250814708136 0.0 0.0 1.1621740724145393 0.0 0.0 0.284719721287 1.0 0.500838355314 0.99811176147 0.882097980488 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0640985086739785 0.0 0.0 1.117461730570291 0.0 0.0 0.348756760091 1.0 0.536913073775 0.774445780539 0.675305843363 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 4381 248 -922.2871044958234 1.5942904654617835 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.042590165236429 0.0 0.0 1.0794243513970183 0.0 0.0 0.376216322989 1.0 0.514595462088 0.816190890935 0.906087570539 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

4500 15000

4500

4500 15000

4500

4500 15000

4500

4500 15000

4500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0213442122555483 0.0 0.0 0.9796305505702649 0.0 0.0 0.289092972904 1.0 0.516572715513 1.38525570587 0.947698431445 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 4680 211 -3143.4122315054674 6.700607274667562 0.0

zeta acceptanceProb 0 4681 211 -23732.75925964529 6.703556097778439 0.0

zeta acceptanceProb 0 4682 211 -13275.48167452627 6.664364772567335 0.0

xi acceptanceProb 0 4683 211 -22994.931187721242 6.481778327790524 0.0

zeta acceptanceProb 0 4683 211 -6065.0009742597 6.664364772567335 0.0

xi acceptanceProb 0 4684 211 -2538.985156036406 6.5828110353096285 0.0

zeta acceptanceProb 0 4684 211 -10284.619824696894 6.685039781744975 0.0

xi acceptanceProb 0 4685 211 -945.2117080275735 6.5543719560450056 0.0

zeta acceptanceProb 0 4685 211 -1316.714285444431 6.774298860662539 0.0

xi acceptanceProb 0 4686 211 -846.6115712086737 6.576886597347126 0.0

zeta acceptanceProb 0 4687 211 -1403.1160295260102 6.735646715759986 0.0

xi acceptanceProb 0 4688 211 -11459.3006298651 6.387329995246704 0.0

xi acceptanceProb 0 4689 211 -3688.153018955835 6.338096853446365 0.0

zeta acceptanceProb 0 4689 211 -4176.280432408575 6.756990626915719 0.0

xi acceptanceProb 0 4690 211 -3915.711560446165 6.320745079709903 0.0

zeta acceptanceProb 0 4691 211 -11079.706043178992 6.7791381359542395 0.0

zeta acceptanceProb 0 4692 211 -31475.459841652555 6.7292163869169555 0.0

xi acceptanceProb 0 4693 211 -4091.255626873887 6.020938819625895 0.0

zeta acceptanceProb 0 4694 211 -3000.009162974322 6.7387581211380025 0.0

zeta acceptanceProb 0 4695 211 -4553.488424114522 6.737864354448997 0.0

xi acceptanceProb 0 4696 211 -2567.4154123436806 6.0942402301009535 0.0

xi acceptanceProb 0 4697 211 -850.7499193810721 6.111586766242111 0.0

zeta acceptanceProb 0 4697 211 -8969.48911966094 6.812623797082334 0.0

xi acceptanceProb 0 4698 211 -4308.706108119434 6.18782372426015 0.0

zeta acceptanceProb 0 4698 211 -69820.05571914547 6.050432078529487 0.0

zeta acceptanceProb 0 4699 211 -32925.739349234515 6.041929379150647 0.0

zeta acceptanceProb 0 4700 211 -27837.313361706194 6.019498986528329 0.0

xi acceptanceProb 0 4701 211 -1421.8031765393132 6.7916334330493715 0.0

zeta acceptanceProb 0 4701 211 -2720.4356040854377 -1.233033738295146 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9697890241523612 0.0 0.0 0.917531352429455 0.0 0.0 0.312310494024 1.0 0.540453384934 1.19436411769 0.842496377039 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9182171931064765 0.0 0.0 1.009193864796955 0.0 0.0 0.249795116116 1.0 0.532336645076 0.831778670321 0.814062579315 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9182171931064765 0.0 0.0 1.009193864796955 0.0 0.0 0.287893205147 1.0 0.500753875323 0.927724123211 0.762772442009 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9182171931064765 0.0 0.0 1.009193864796955 0.0 0.0 0.260183051827 1.0 0.528416638274 0.757895092281 0.782332262826 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9370911385646796 0.0 0.0 1.0660033715686537 0.0 0.0 0.252420570339 1.0 0.550472229238 0.962866401474 0.694786360065 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0807937135246617 0.0 0.0 1.1955313245476937 0.0 0.0 0.236226609384 1.0 0.536936551503 0.611329271441 0.919490185846 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0807937135246617 0.0 0.0 1.1955313245476937 0.0 0.0 0.236226609384 1.0 0.503394897333 0.542710169783 0.947305679756 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0807937135246617 0.0 0.0 1.1955313245476937 0.0 0.0 0.243615659072 1.0 0.516290073504 0.691893533153 0.947305679756 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 4896 211 -9286.621635098389 0.7958460417750173 0.0

5000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.142599365000258 0.0 0.0 1.103848294918769 0.0 0.0 0.207458858132 1.0 0.52328842439 0.916278845144 0.773257902992 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.183006610213876 0.0 0.0 1.1787127719314807 0.0 0.0 0.205912786254 1.0 0.550506426074 0.897774346932 0.881567306735 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 5202 260 -1569.373507030836 3.6277400659714987 0.0

eta acceptanceProb 0 5203 260 -821.436146835637 3.6135790512271297 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.159668871881797 0.0 0.0 1.0934118912135444 0.0 0.0 0.257255224375 1.0 0.53067555303 0.630463018466 0.999124633864 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1071276282078146 0.0 0.0 1.0221057634185722 0.0 0.0 0.268711064733 1.0 0.528913671635 1.00492982813 0.792433035214 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1071276282078146 0.0 0.0 1.0221057634185722 0.0 0.0 0.240588522853 1.0 0.524671021666 0.942896760415 0.792433035214 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9109564670227277 0.0 0.0 0.9064597029441281 0.0 0.0 0.232622469732 1.0 0.501778353474 0.93318145744 0.900166485194 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 5228 248 -746.6069459091523 1.3110148629613843 0.0

xi acceptanceProb 0 5240 73 -134986.44428973735 0.6788903157075908 0.0

zeta acceptanceProb 0 5240 73 -2700.5650352521434 1.3087571382124992 0.0

zeta acceptanceProb 0 5283 248 -871.1699457624386 1.571865973780426 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8358293326127866 0.0 0.0 0.8528471404110428 0.0 0.0 0.276476211214 1.0 0.509410144563 1.00738325046 0.700279990662 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 5365 172 -841.8811152957743 -1.3848441353743437 0.0

zeta acceptanceProb 0 5366 172 -1363.663534858995 -1.3843290781166337 0.0

zeta acceptanceProb 0 5370 172 -2203.201203040138 0.07622059429085767 0.0

zeta acceptanceProb 0 5375 172 -1484.295112658749 -0.47303604055734905 0.0

zeta acceptanceProb 0 5381 172 -3369.413195841104 -2.290007358466484 0.0

zeta acceptanceProb 0 5386 172 -934.78047598877 0.04964620582409479 0.0

zeta acceptanceProb 0 5392 172 -1755.7380512686216 -1.874746701614762 0.0

zeta acceptanceProb 0 5396 172 -1366.6930850473848 -0.8618532918378425 0.0

zeta acceptanceProb 0 5416 172 -2309.6410232465782 -3.9002280161054914 0.0

zeta acceptanceProb 0 5426 172 -927.2584238411664 -3.456807500842719 0.0

zeta acceptanceProb 0 5434 172 -875.8476839136429 -1.857775438478194 0.0

zeta acceptanceProb 0 5480 172 -1597.3681338154868 -1.1363652365754269 0.0

zeta acceptanceProb 0 5481 172 -893.8292937795939 -0.8998222656656251 0.0

zeta acceptanceProb 0 5483 172 -1754.5038800525087 -0.6585403053733967 0.0

5500

zeta acceptanceProb 0 5500 172 -768.7065736386892 0.11739847780288376 0.0

5500

zeta acceptanceProb 0 5506 172 -760.2233432001656 -1.9263639269482302 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8041720856695823 0.0 0.0 0.8716749668123188 0.0 0.0 0.137456590972 1.0 0.535371839714 1.2185071603 0.842062683563 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 5511 172 -820.2574161886447 0.7187217322720594 0.0

eta acceptanceProb 0 5548 72 -952.0016081650917 1.7204488458889586 0.0

zeta acceptanceProb 0 5553 172 -1288.2042353638676 0.5257918573985046 0.0

eta acceptanceProb 0 5594 72 -790.7718067818823 1.80000252048409 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8907894501059804 0.0 0.0 1.0311844896283269 0.0 0.0 0.143858403074 1.0 0.502315235869 1.14830186935 0.824380228026 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8907894501059804 0.0 0.0 1.0311844896283269 0.0 0.0 0.148716110853 1.0 0.512404446152 1.14830186935 0.824380228026 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8847852490182763 0.0 0.0 1.0456466098504804 0.0 0.0 0.176863716461 1.0 0.543160883934 0.991181389209 0.880147782193 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 5724 172 -1079.3713642262132 0.29683191728126757 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8435815175108542 0.0 0.0 0.9216705248199071 0.0 0.0 0.137977721525 1.0 0.545611740332 0.939530877955 0.808354623169 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8138174525221205 0.0 0.0 0.9083279021928498 0.0 0.0 0.195927081626 1.0 0.531402152957 1.21356268883 0.957515529106 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

6000 15000

6000

6000 15000

6000

6000 15000

6000

6000 15000

6000

Traceback (most recent call last):

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/formatters.py", line 334, in __call__

return printer(obj)

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 247, in <lambda>

png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 131, in print_figure

fig.canvas.print_figure(bytes_io, **kw)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backend_bases.py", line 2212, in print_figure

**kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 517, in print_png

FigureCanvasAgg.draw(self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 437, in draw

self.figure.draw(self.renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/figure.py", line 1493, in draw

renderer, self, artists, self.suppressComposite)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.py", line 2635, in draw

mimage._draw_list_compositing_images(renderer, self, artists)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/legend.py", line 775, in draw

bbox = self._legend_box.get_window_extent(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 266, in get_window_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 831, in get_extent

bbox, info, d = self._text._get_layout(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/text.py", line 309, in _get_layout

ismath=ismath)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 236, in get_text_width_height_descent

s, fontsize, renderer=self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 501, in get_text_width_height_descent

dvifile = self.make_dvi(tex, fontsize)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 362, in make_dvi

with Locked(self.texcache):

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/cbook/__init__.py", line 2529, in __enter__

raise self.TimeoutError(err_str)

TimeoutError: LOCKERROR: matplotlib is trying to acquire the lock

'/Users/lester/.matplotlib/tex.cache/.matplotlib_lock-*'

and has failed. This maybe due to any other process holding this

lock. If you are sure no other matplotlib process is running try

removing these folders and trying again.

zeta acceptanceProb 0 6027 211 -21712.897052366243 2.766988273875847 0.0

xi acceptanceProb 0 6028 211 -3872.4710096800995 1.9798069136695102 0.0

zeta acceptanceProb 0 6028 211 -7038.719808084215 2.5979726100714715 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7104043859471993 0.0 0.0 0.7996916346305658 0.0 0.0 0.135128341577 1.0 0.608853853301 1.02715151621 0.912060487039 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 6128 231 -940.22460844348 1.107817345498851 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0780189121895272 0.0 0.0 1.1299233906903912 0.0 0.0 0.196972510588 1.0 0.508062726456 0.819596726095 0.716056513176 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8727916263529221 0.0 0.0 0.9464976311560112 0.0 0.0 0.189352319528 1.0 0.504180135158 0.881337040294 0.803962167949 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8727916263529221 0.0 0.0 0.9464976311560112 0.0 0.0 0.189352319528 1.0 0.510228175263 0.881337040294 0.803962167949 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

6500

xi acceptanceProb 0 6543 211 -1138.4378868806668 2.6385417899896915 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6999038929994311 0.0 0.0 0.7589210396651986 0.0 0.0 0.20119818209 1.0 0.500780672484 1.075234888 1.06840901317 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0239254469178807 0.0 0.0 1.0224498224218956 0.0 0.0 0.250638935633 1.0 0.538627216069 0.715272251754 0.851332759634 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9579624645304214 0.0 0.0 1.0299658199462483 0.0 0.0 0.261397574313 1.0 0.53017438652 1.00577526013 0.818294036953 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.760446705977602 0.0 0.0 0.8653771966863597 0.0 0.0 0.175552940997 1.0 0.502893793748 0.983208126159 0.865312844743 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

7000

zeta acceptanceProb 0 7022 224 -770.1880701121601 3.4155278667996765 0.0

zeta acceptanceProb 0 7203 224 -833.0709923138598 3.630793432427148 0.0

zeta acceptanceProb 0 7244 224 -749.4055210522627 3.609382853158464 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9433874627372739 0.0 0.0 0.9535492727799977 0.0 0.0 0.32831173544 1.0 0.521175575654 1.06920449912 0.847186341311 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

xi acceptanceProb 0 7306 73 -9576.261861585788 1.5167984783143158 0.0

eta acceptanceProb 0 7306 73 -982.0784100841777 2.5773954148262455 0.0

zeta acceptanceProb 0 7306 73 -11751.128109714758 2.5773954148262455 0.0

7500 15000

7500

7500 15000

7500

7500 15000

7500

7500 15000

7500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6942467887094136 0.0 0.0 0.7320974059590243 0.0 0.0 0.193330726989 1.0 0.560022585497 0.828246588021 0.723847535572 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7501006453832143 0.0 0.0 0.819724367453454 0.0 0.0 0.15847619721 1.0 0.501813264018 0.870587966463 0.868493583908 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8268712287634481 0.0 0.0 0.8778430326338004 0.0 0.0 0.191541497311 1.0 0.543440388235 0.969056216425 0.772253447547 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9158232116608906 0.0 0.0 0.8970395980629728 0.0 0.0 0.233788670219 1.0 0.508479037033 0.96586210881 0.833460018246 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 7985 260 -987.5515314312602 1.7833072311905762 0.0

8000

eta acceptanceProb 0 7992 260 -2485.773354822697 1.7033196264278203 0.0

eta acceptanceProb 0 7993 260 -1455.2351843372244 2.1234447306372535 0.0

eta acceptanceProb 0 7996 260 -3132.9607121419654 1.8208419464877301 0.0

8000

eta acceptanceProb 0 8002 260 -915.9752945178506 1.1775262183413036 0.0

eta acceptanceProb 0 8005 260 -2090.3168744827726 0.834533948372647 0.0

eta acceptanceProb 0 8006 260 -1781.7789474434792 0.7936915974962415 0.0

8000

eta acceptanceProb 0 8011 260 -1597.1706660851737 3.1571802165102563 0.0

eta acceptanceProb 0 8013 260 -1917.4704772553462 3.295956983425326 0.0

eta acceptanceProb 0 8024 260 -1277.759473960268 3.5877360535298513 0.0

eta acceptanceProb 0 8027 260 -1051.309012435734 3.8427591586096046 0.0

eta acceptanceProb 0 8033 260 -1925.110128181913 3.835032026395849 0.0

eta acceptanceProb 0 8036 260 -1201.3691727715936 3.598166026847527 0.0

eta acceptanceProb 0 8038 260 -785.5631203654723 2.500180638269372 0.0

eta acceptanceProb 0 8046 72 -1097.0923088786767 1.7042923952890532 0.0

eta acceptanceProb 0 8052 260 -1862.4642243586857 4.036997969851522 0.0

eta acceptanceProb 0 8053 260 -1087.22144009504 3.992313564256416 0.0

eta acceptanceProb 0 8065 260 -1097.2638397929095 3.155742908925081 0.0

eta acceptanceProb 0 8074 260 -1140.6081973316261 2.8203458192213735 0.0

eta acceptanceProb 0 8079 260 -1251.5068912624351 4.011842624210485 0.0

eta acceptanceProb 0 8088 260 -1217.097366228503 4.230519787782295 0.0

eta acceptanceProb 0 8108 260 -2055.589947122662 4.032704353905541 0.0

eta acceptanceProb 0 8117 260 -1017.2935712569217 0.8593372609391555 0.0

xi acceptanceProb 0 8204 73 -31638.229446968817 1.3914389826099867 0.0

zeta acceptanceProb 0 8204 73 -30039.805656234395 2.336662056716434 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9487671333462149 0.0 0.0 1.0137528739957522 0.0 0.0 0.268480016211 1.0 0.502566774965 0.900716894694 0.863832948288 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8709340875670627 0.0 0.0 0.9047500439191901 0.0 0.0 0.268480016211 1.0 0.523053131821 0.900716894694 0.863832948288 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

xi acceptanceProb 0 8415 211 -12651.403993353157 0.4331655897958555 0.0

zeta acceptanceProb 0 8415 211 -7216.565081535901 3.4592758608288072 0.0

zeta acceptanceProb 0 8416 211 -35408.84939794765 3.485684381535539 0.0

xi acceptanceProb 0 8417 211 -1764.7271289576006 3.1907809563409986 0.0

zeta acceptanceProb 0 8417 211 -57905.27003948132 4.065142728941436 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1448929333548958 0.0 0.0 1.1620884716735795 0.0 0.0 0.297726300121 1.0 0.516794311515 1.02423553445 1.00821661779 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

8500

xi acceptanceProb 0 8501 73 -135683.64135414545 1.7526423490392464 0.0

zeta acceptanceProb 0 8501 73 -5288.1879878508225 3.1383908114762944 0.0

xi acceptanceProb 0 8502 73 -3497.3215759910195 2.0101838981461913 0.0

8500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7785421407532337 0.0 0.0 0.8552252787153977 0.0 0.0 0.179077283537 1.0 0.5846271263 1.2012762943 0.796688918561 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.958204103179268 0.0 0.0 0.9710032472743664 0.0 0.0 0.14981116782 1.0 0.622088429619 1.11405307407 0.771821653419 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0688542850740608 0.0 0.0 1.090777544345387 0.0 0.0 0.228386314987 1.0 0.601161482121 1.02189353913 0.816684815444 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8448508404283257 0.0 0.0 0.9006575885353935 0.0 0.0 0.213120360549 1.0 0.576426454402 0.932600416046 0.866161740838 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

9000 15000

9000

9000 15000

9000

9000 15000

9000

9000 15000

9000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.82702525207346 0.0 0.0 0.937434141256246 0.0 0.0 0.142974500222 1.0 0.562717102295 0.98080785236 0.687101861 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7794720042991907 0.0 0.0 0.9107489306336722 0.0 0.0 0.142974500222 1.0 0.553484682237 0.888454404682 0.687101861 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7794720042991907 0.0 0.0 0.9107489306336722 0.0 0.0 0.175376396969 1.0 0.542426000822 1.12846516751 0.723625408986 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8555304022992948 0.0 0.0 0.9569999006795352 0.0 0.0 0.165925617719 1.0 0.501277167365 0.878480361446 0.836203921402 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8555304022992948 0.0 0.0 0.9569999006795352 0.0 0.0 0.165925617719 1.0 0.555160948837 0.878480361446 0.910407841148 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8555304022992948 0.0 0.0 0.9569999006795352 0.0 0.0 0.129625632839 1.0 0.540182636888 0.994569855992 0.80944287174 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8928745712575603 0.0 0.0 0.9506851269386687 0.0 0.0 0.120048738833 1.0 0.522952941595 0.916980114749 0.743877194473 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8928745712575603 0.0 0.0 0.9506851269386687 0.0 0.0 0.120048738833 1.0 0.580196868941 0.916980114749 0.743877194473 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8928745712575603 0.0 0.0 0.9506851269386687 0.0 0.0 0.0934132676983 1.0 0.529728011904 0.916980114749 0.743877194473 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8928745712575603 0.0 0.0 0.9506851269386687 0.0 0.0 0.0865210259483 1.0 0.586425372383 0.920851463245 0.782372824745 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8928745712575603 0.0 0.0 0.9506851269386687 0.0 0.0 0.0881100924729 1.0 0.528581427217 0.849469201389 0.717250944306 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8928745712575603 0.0 0.0 0.9506851269386687 0.0 0.0 0.0881100924729 1.0 0.511897664484 0.925959833757 0.757632618386 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8928745712575603 0.0 0.0 0.9506851269386687 0.0 0.0 0.0881100924729 1.0 0.58715881247 0.781395408677 0.757632618386 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7223155127484095 0.0 0.0 0.8653815973574387 0.0 0.0 0.0809668724459 1.0 0.515845191607 0.754702801315 0.942195487363 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7223155127484095 0.0 0.0 0.8653815973574387 0.0 0.0 0.0844113674868 1.0 0.500413986125 1.08598682512 0.754209726304 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8928745712575603 0.0 0.0 0.9506851269386687 0.0 0.0 0.0943766307918 1.0 0.508062288647 0.777137931708 0.766952619998 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8928745712575603 0.0 0.0 0.9506851269386687 0.0 0.0 0.0990017091961 1.0 0.514076664831 0.698704530191 0.831366848246 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8455971578134349 0.0 0.0 0.9303618889790916 0.0 0.0 0.0930541606882 1.0 0.588289071903 0.735673106552 0.831366848246 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8455971578134349 0.0 0.0 0.9303618889790916 0.0 0.0 0.0929730857561 1.0 0.517777682897 0.735673106552 0.831366848246 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.815876731051644 0.0 0.0 0.8877691626597138 0.0 0.0 0.116735483766 1.0 0.528651535964 0.999154084133 0.704400283131 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.815876731051644 0.0 0.0 0.8877691626597138 0.0 0.0 0.107357274816 1.0 0.510131097758 0.883655158274 0.757031944301 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.815876731051644 0.0 0.0 0.8877691626597138 0.0 0.0 0.107357274816 1.0 0.519896021988 0.901281810325 0.757031944301 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6988824132116784 0.0 0.0 0.8456886013614711 0.0 0.0 0.0964096127925 1.0 0.510310952106 0.984705466026 0.83163879254 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6558490328049243 0.0 0.0 0.8137205146245752 0.0 0.0 0.0837214556358 1.0 0.554705052542 1.0008798097 0.760093677705 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6769252745466571 0.0 0.0 0.830820474163775 0.0 0.0 0.0873600714591 1.0 0.546240486282 0.932305765087 0.776237247045 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6769252745466571 0.0 0.0 0.830820474163775 0.0 0.0 0.0873600714591 1.0 0.508558557939 0.865395190121 0.735822956813 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6691702479547746 0.0 0.0 0.8392199004675444 0.0 0.0 0.0818439784539 1.0 0.556493739461 0.920216790462 0.85743246924 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6691702479547746 0.0 0.0 0.8392199004675444 0.0 0.0 0.0668604647722 1.0 0.519339601151 0.814307681268 0.792313305934 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6691702479547746 0.0 0.0 0.8392199004675444 0.0 0.0 0.0715607438204 1.0 0.561952877514 0.814307681268 0.792313305934 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6701989575469534 0.0 0.0 0.8015368057959775 0.0 0.0 0.136864419348 1.0 0.515383414279 1.03565828052 0.882442539863 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6682869601283213 0.0 0.0 0.7946155172467964 0.0 0.0 0.129631687889 1.0 0.547620345797 0.834331094632 0.780619678655 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9275281967386808 0.0 0.0 0.9838483978760046 0.0 0.0 0.189336396797 1.0 0.516792288573 0.845323729756 0.836921703597 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7043292353884228 0.0 0.0 0.8434219168416001 0.0 0.0 0.0996028577175 1.0 0.514599892452 0.925723979267 0.774002781383 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7508569153165827 0.0 0.0 0.8702283622078358 0.0 0.0 0.0955173265497 1.0 0.512340914089 1.07279488135 0.886594084906 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.986082079680868 0.0 0.0 1.153504559111992 0.0 0.0 0.176556665155 1.0 0.584606436725 0.863827856012 0.695980788574 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7388342860480352 0.0 0.0 0.8366371192614144 0.0 0.0 0.14151275451 1.0 0.576563749282 0.946621564843 0.979165410893 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9346594658461513 0.0 0.0 1.0659854502034782 0.0 0.0 0.180791814892 1.0 0.583397809565 0.770039937859 0.710761818869 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8915247340606697 0.0 0.0 1.0245794071981034 0.0 0.0 0.172542125982 1.0 0.555309197837 0.770039937859 0.670009704568 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0420528801882434 0.0 0.0 1.1330264866174544 0.0 0.0 0.17359779357 1.0 0.526443360979 0.698825762733 0.670009704568 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

xi acceptanceProb 0 9428 73 -21765.87510823267 3.8155070441965164 0.0

zeta acceptanceProb 0 9428 73 -796.7968266988676 4.013070123701284 0.0

zeta acceptanceProb 0 9429 73 -2445.7203064024943 4.128421669908724 0.0

9500

zeta acceptanceProb 0 9503 205 -2862.1467162608456 0.7862280798659531 0.0

xi acceptanceProb 0 9504 205 -1861.423095531098 1.0231212099966465 0.0

zeta acceptanceProb 0 9504 205 -4212.566325084315 0.9083137914898505 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0240379053962987 0.0 0.0 0.9652542466362375 0.0 0.0 0.266791687489 1.0 0.550126402263 0.880281175942 0.814831539586 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7980301180264017 0.0 0.0 0.8780741973213645 0.0 0.0 0.133235963193 1.0 0.505718832247 1.08008785078 0.859386313915 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 9731 172 -753.8090068080529 -3.388807405374486 0.0

xi acceptanceProb 0 9767 73 -13654.226448323147 2.776327019238472 0.0

zeta acceptanceProb 0 9780 172 -1343.4636445447293 -1.308650898351819 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6714551138902302 0.0 0.0 0.7791514530656569 0.0 0.0 0.142256413493 1.0 0.561812777545 0.780758880258 1.02862446924 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6714551138902302 0.0 0.0 0.7791514530656569 0.0 0.0 0.136418074319 1.0 0.501019562006 0.780758880258 0.876245179093 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8009512961530907 0.0 0.0 0.8746972882237303 0.0 0.0 0.169257849621 1.0 0.500110654746 1.25698126284 0.874117672532 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

xi acceptanceProb 0 9970 73 -100734.41724361695 3.568961625936857 0.0

zeta acceptanceProb 0 9970 73 -8503.012355415864 4.011830247099299 0.0

10000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9583336719027294 0.0 0.0 0.9811258607868568 0.0 0.0 0.167083740374 1.0 0.534342858881 0.949600193247 0.833798237891 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9583336719027294 0.0 0.0 0.9811258607868568 0.0 0.0 0.184875930271 1.0 0.504858289604 0.956400057227 0.821009971894 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9042340247692314 0.0 0.0 0.9648337263614847 0.0 0.0 0.187399803904 1.0 0.511476071922 0.748586825221 0.821009971894 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

10000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.012257877495894 0.0 0.0 1.1032857432973413 0.0 0.0 0.208440314072 1.0 0.571034682383 0.99273435382 0.84050089987 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 10034 260 -3277.161819719745 3.3611783032944613 0.0

eta acceptanceProb 0 10038 260 -1571.390376672819 2.8776702329881454 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.729875167314877 0.0 0.0 0.7908414662104224 0.0 0.0 0.21926259129 1.0 0.505889255872 1.41492904556 0.879266586281 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

xi acceptanceProb 0 10230 73 -4407.414926675546 -2.021723055650253 0.0

zeta acceptanceProb 0 10230 73 -7351.813117947478 2.89480009093917 0.0

xi acceptanceProb 0 10231 73 -34964.72099089987 3.0938510922324682 0.0

zeta acceptanceProb 0 10231 73 -12962.254445771681 3.024534614192453 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8253800812703422 0.0 0.0 0.8802090745487641 0.0 0.0 0.211293987392 1.0 0.511527789654 1.3471616886 0.810086788861 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

10500 15000

10500

10500 15000

10500

10500 15000

10500

10500 15000

10500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9802880160328864 0.0 0.0 0.9858996914978297 0.0 0.0 0.248906626852 1.0 0.510450891683 0.82896246238 0.867517926752 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 10533 248 -978.2758696930066 2.220318429232857 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8276098431897705 0.0 0.0 0.8853273627436189 0.0 0.0 0.184724539732 1.0 0.530596707398 1.35413103667 0.821153359061 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9351562608786599 0.0 0.0 0.9584650333448208 0.0 0.0 0.281085231114 1.0 0.557148542055 0.902331924205 1.06313286652 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9351562608786599 0.0 0.0 0.9584650333448208 0.0 0.0 0.275111423897 1.0 0.544602783194 0.902331924205 0.932804644321 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8556299233248221 0.0 0.0 0.8949720844015173 0.0 0.0 0.206441332618 1.0 0.508685840302 0.735654218193 0.915638597779 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

xi acceptanceProb 0 10907 73 -138144.36581487974 1.8318525839880637 0.0

11000

zeta acceptanceProb 0 11055 32 -901.3811741253454 4.392112940023855 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0411447051210776 0.0 0.0 1.1683588458204617 0.0 0.0 0.221106637122 1.0 0.679093837005 0.886350942705 0.748868438583 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0190944233154882 0.0 0.0 1.1538608046524756 0.0 0.0 0.226312781561 1.0 0.520751516488 0.841547519459 0.867816605111 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 11104 32 -755.583514879812 1.8801696673575459 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0450673165131596 0.0 0.0 1.164526335722486 0.0 0.0 0.238886754061 1.0 0.549834541939 0.704308314945 0.827948040063 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1220705117766434 0.0 0.0 1.2406559665550108 0.0 0.0 0.227637692508 1.0 0.504605470651 0.711122011109 0.737264320693 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1098790742727411 0.0 0.0 1.0356012419557064 0.0 0.0 0.283869237607 1.0 0.52969993388 0.640895039595 0.875782785725 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

xi acceptanceProb 0 11278 73 -7379.164281678934 0.8652812458728693 0.0

zeta acceptanceProb 0 11278 73 -9303.30762150363 1.1881332416338821 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8983824072075104 0.0 0.0 0.9256625665827453 0.0 0.0 0.274034833042 1.0 0.649271183083 0.76116068224 0.752312316205 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

11500

zeta acceptanceProb 0 11479 248 -757.3865497043611 1.6144243834029743 0.0

11500

zeta acceptanceProb 0 11631 211 -3540.9293625592836 2.4193361660263752 0.0

zeta acceptanceProb 0 11632 211 -24024.85216063058 2.447899017611522 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9272787535273078 0.0 0.0 0.953724138361109 0.0 0.0 0.200419374487 1.0 0.550892925119 0.814922295665 0.954306320149 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

xi acceptanceProb 0 11771 73 -3641.5088143698567 1.9126521065003257 0.0

zeta acceptanceProb 0 11771 73 -2877.2261877707524 2.3258752807857643 0.0

eta acceptanceProb 0 11887 72 -769.5203389302745 3.460791023357793 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8395143209579615 0.0 0.0 0.8866011132452147 0.0 0.0 0.190394593876 1.0 0.512801858925 1.01176822794 0.812415148625 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

xi acceptanceProb 0 11928 73 -57590.30692247233 3.8952475904346744 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.7118981957456076 0.0 0.0 1.7238780895201131 0.0 0.0 0.404513733036 1.0 0.532449162851 0.398359304735 0.521913857026 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

xi acceptanceProb 0 11953 73 -11176.613969787777 2.748377075938125 0.0

zeta acceptanceProb 0 11953 73 -1575.1032241609112 2.8198419817469556 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.7118981957456076 0.0 0.0 1.7238780895201131 0.0 0.0 0.399370249736 1.0 0.648125231392 0.398359304735 0.521913857026 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.7191784106887111 0.0 0.0 1.6735615463500966 0.0 0.0 0.420833733215 1.0 0.599377268029 0.431083982068 0.521913857026 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

12000 15000

12000

12000 15000

12000

12000 15000

12000

12000 15000

12000

Traceback (most recent call last):

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/formatters.py", line 334, in __call__

return printer(obj)

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 247, in <lambda>

png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 131, in print_figure

fig.canvas.print_figure(bytes_io, **kw)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backend_bases.py", line 2212, in print_figure

**kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 517, in print_png

FigureCanvasAgg.draw(self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 437, in draw

self.figure.draw(self.renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/figure.py", line 1493, in draw

renderer, self, artists, self.suppressComposite)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.py", line 2635, in draw

mimage._draw_list_compositing_images(renderer, self, artists)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/legend.py", line 775, in draw

bbox = self._legend_box.get_window_extent(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 266, in get_window_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 831, in get_extent

bbox, info, d = self._text._get_layout(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/text.py", line 309, in _get_layout

ismath=ismath)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 236, in get_text_width_height_descent

s, fontsize, renderer=self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 501, in get_text_width_height_descent

dvifile = self.make_dvi(tex, fontsize)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 362, in make_dvi

with Locked(self.texcache):

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/cbook/__init__.py", line 2529, in __enter__

raise self.TimeoutError(err_str)

TimeoutError: LOCKERROR: matplotlib is trying to acquire the lock

'/Users/lester/.matplotlib/tex.cache/.matplotlib_lock-*'

and has failed. This maybe due to any other process holding this

lock. If you are sure no other matplotlib process is running try

removing these folders and trying again.

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8268099623542863 0.0 0.0 0.9437613115892594 0.0 0.0 0.185011743829 1.0 0.506114435316 0.781738404135 0.828264570892 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 12269 211 -3300.5434635245133 4.088609820498082 0.0

zeta acceptanceProb 0 12324 32 -840.672901063105 5.632955614413617 0.0

12500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8037748895302474 0.0 0.0 0.880838738509384 0.0 0.0 0.193845731057 1.0 0.594422856793 1.05473846721 0.82664626487 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8822444707958802 0.0 0.0 0.9408379327975358 0.0 0.0 0.236000512723 1.0 0.583193265006 0.930668207448 0.950851824079 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8564758581173663 0.0 0.0 0.9489539133125913 0.0 0.0 0.187377639255 1.0 0.54941510534 0.90463572359 0.961480510779 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.740436870153335 0.0 0.0 0.8390456292911878 0.0 0.0 0.207963486645 1.0 0.502973056983 0.984273456554 0.92115323556 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7961220292637476 0.0 0.0 0.8899803974963105 0.0 0.0 0.170423357905 1.0 0.527290465306 1.12553376449 0.663597164562 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

xi acceptanceProb 0 12705 211 -881.259897861221 0.78935338581854 0.0

zeta acceptanceProb 0 12707 211 -8288.00114022636 1.7119836420003718 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7185658671299052 0.0 0.0 0.8789345055108624 0.0 0.0 0.137593978931 1.0 0.601895008086 0.888232732644 0.696671305794 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6744019899664235 0.0 0.0 0.8619719808943314 0.0 0.0 0.108822811984 1.0 0.51811797539 0.862112799082 0.858660721911 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6744019899664235 0.0 0.0 0.8619719808943314 0.0 0.0 0.105047469163 1.0 0.582836112181 0.862112799082 0.830624938762 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6744019899664235 0.0 0.0 0.8619719808943314 0.0 0.0 0.104516284357 1.0 0.605917104606 0.862112799082 0.830624938762 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6744019899664235 0.0 0.0 0.8619719808943314 0.0 0.0 0.104516284357 1.0 0.521920169447 0.730305060308 0.878995064928 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6644951802722923 0.0 0.0 0.852040972575708 0.0 0.0 0.0805936170583 1.0 0.5229219357 0.748767603204 0.881442619284 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6644951802722923 0.0 0.0 0.852040972575708 0.0 0.0 0.0805936170583 1.0 0.55852908351 0.748767603204 0.859448799216 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6644951802722923 0.0 0.0 0.852040972575708 0.0 0.0 0.0770258055156 1.0 0.528376279379 0.861907215026 0.859448799216 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6644951802722923 0.0 0.0 0.852040972575708 0.0 0.0 0.0770258055156 1.0 0.504781793652 0.889166331506 0.859448799216 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7053422800360609 0.0 0.0 0.8756315954508153 0.0 0.0 0.0766470585834 1.0 0.502159109296 0.889166331506 0.805166855951 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6987301776126624 0.0 0.0 0.8466611497061831 0.0 0.0 0.0775128429418 1.0 0.68312212475 0.889166331506 0.75605125815 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6925268676543892 0.0 0.0 0.8468332401051698 0.0 0.0 0.0835285025043 1.0 0.541413603399 0.749892602813 0.79822137905 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7230350801906786 0.0 0.0 0.8665082403258728 0.0 0.0 0.0692702227167 1.0 0.597654430916 0.711596508299 0.783191904734 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7011610983925833 0.0 0.0 0.8305298338472545 0.0 0.0 0.0908538924024 1.0 0.529083505042 0.711596508299 0.669040485894 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0809307692922439 0.0 0.0 1.0587152714627635 0.0 0.0 0.350546660296 1.0 0.512637369189 0.699724109356 0.895362050513 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7381292969534867 0.0 0.0 0.8735376263665827 0.0 0.0 0.14567602635 1.0 0.528049048301 0.754707664272 0.774881719392 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.758957389019039 0.0 0.0 0.8286234738039052 0.0 0.0 0.172045401029 1.0 0.519248638069 1.07419793326 0.627726852322 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

13000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.776025741590266 0.0 0.0 0.8167098156539941 0.0 0.0 0.177353145889 1.0 0.52152114116 1.02012556274 0.692939979744 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

13000

zeta acceptanceProb 0 13024 61 -776.7317174760061 5.285939633911037 0.0

zeta acceptanceProb 0 13045 61 -1232.705815126329 4.190022364567461 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9962375722237282 0.0 0.0 0.983610871489214 0.0 0.0 0.237730181358 1.0 0.504768041836 0.971055610346 0.689849807733 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.669231230154786 0.0 0.0 0.7478397096327212 0.0 0.0 0.245689055717 1.0 0.540041570143 0.909988292238 0.784339287175 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 13384 248 -854.624847979204 -0.25919887320709095 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6658037760790694 0.0 0.0 0.7944983675113878 0.0 0.0 0.166148994995 1.0 0.507672993383 0.804045041801 0.718347501649 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7064511239078978 0.0 0.0 0.8237089071459783 0.0 0.0 0.128776012399 1.0 0.54154061368 0.956243715839 0.859059114969 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7510049586308192 0.0 0.0 0.8246279477081168 0.0 0.0 0.160820179766 1.0 0.514824350978 0.880928572302 0.816147058647 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6934448030671054 0.0 0.0 0.7551308353369207 0.0 0.0 0.154062300549 1.0 0.514746256793 0.871336294674 0.80054098736 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6935313660274832 0.0 0.0 0.7437530131662954 0.0 0.0 0.163827656149 1.0 0.601310226385 0.91942005504 0.778951302785 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

13500 15000

13500

13500 15000

13500

13500 15000

13500

13500 15000

13500

zeta acceptanceProb 0 13554 248 -770.7487495884868 1.8576102538183425 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7420532928106934 0.0 0.0 0.8864232063883236 0.0 0.0 0.179880389232 1.0 0.523851044944 0.993739226816 0.741888391656 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 13618 211 -822.2222271666279 0.5171588905401457 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8393792377776021 0.0 0.0 0.923784734495583 0.0 0.0 0.126285717945 1.0 0.524849567588 1.08874353893 0.751063892815 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8395808359512232 0.0 0.0 0.9009128562638814 0.0 0.0 0.133672392098 1.0 0.536914721811 1.08874353893 0.719974198335 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.6872982779560141 0.0 0.0 0.7820212523037837 0.0 0.0 0.124659709301 1.0 0.504327948109 1.15223118365 0.812963468793 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.676509538430193 0.0 0.0 0.7682585697704741 0.0 0.0 0.146064919896 1.0 0.50841465778 1.14268748873 0.84123604239 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 13944 224 -767.5511258398717 3.902342321212149 0.0

xi acceptanceProb 0 13936 73 -3059.9041138394396 3.677686991886383 0.0

zeta acceptanceProb 0 13936 73 -1820.8698030871067 3.8180724674391966 0.0

zeta acceptanceProb 0 13937 73 -3670.3382118872814 3.73071104222906 0.0

14000

xi acceptanceProb 0 14017 73 -21154.611032165314 1.2542317958324487 0.0

zeta acceptanceProb 0 14017 73 -1344.51307625627 1.51873174622123 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7745591161450142 0.0 0.0 0.8311696671119493 0.0 0.0 0.15310904981 1.0 0.568054047747 1.03356908361 0.793982789346 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7745591161450142 0.0 0.0 0.8311696671119493 0.0 0.0 0.127105561936 1.0 0.612764600561 0.772316251765 0.793982789346 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9260895079578256 0.0 0.0 0.8513734347024088 0.0 0.0 0.290087996789 1.0 0.504053239192 1.13482938521 0.777131610729 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0115068153780076 0.0 0.0 1.0333208386109791 0.0 0.0 0.30667445647 1.0 0.523585041867 0.483602758202 0.802495412519 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9182650470562045 0.0 0.0 0.9196522683650623 0.0 0.0 0.232399233125 1.0 0.500569881047 0.94517756853 0.968426930008 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 14286 172 -848.4401974776966 -0.7051982168578914 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0161787262645265 0.0 0.0 0.9425098515552565 0.0 0.0 0.356051456649 1.0 0.503812438356 1.30820161717 0.883628673327 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 14327 172 -770.9582547107245 0.01912839740532235 0.0

eta acceptanceProb 0 14428 260 -755.4512162838259 2.8604238905102886 0.0

zeta acceptanceProb 0 14427 172 -796.067293012628 0.9622139139358536 0.0

14500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.7747837106764746 0.0 0.0 0.8008865095276234 0.0 0.0 0.200086085355 1.0 0.532460900528 1.00428641603 0.924724592637 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

14500

zeta acceptanceProb 0 14533 172 -999.1696845653022 -0.6090130968985326 0.0

eta acceptanceProb 0 14614 72 -799.3765897224422 1.573285108501295 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0776366596315343 0.0 0.0 1.0869480538052145 0.0 0.0 0.2269266868 1.0 0.510030796768 0.868666846509 0.703836899539 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

zeta acceptanceProb 0 14693 32 -865.9058467312 5.044108834154467 0.0

xi acceptanceProb 0 14714 73 -5609.420365593319 0.2608146006949076 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.8834523257322469 0.0 0.0 0.8934711724004363 0.0 0.0 0.299396930835 1.0 0.507399069399 0.856571473213 0.742094890653 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.452177895147413 0.0 0.0 1.3953348578322935 0.0 0.0 0.432567928022 1.0 0.500175365918 0.548314196354 0.568311227194 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.5498494515335655 0.0 0.0 1.4602919564520538 0.0 0.0 0.500000887966 1.0 0.55663398309 0.460308347964 0.667961387103 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.5498494515335655 0.0 0.0 1.4602919564520538 0.0 0.0 0.492619916354 1.0 0.543765928932 0.33910869561 0.567057716176 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.6497183043087527 0.0 0.0 1.6274988537712645 0.0 0.0 0.431110907986 1.0 0.549744450691 0.592942223389 0.492350991989 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.7316618144268578 0.0 0.0 1.6559386314612314 0.0 0.0 0.451860965417 1.0 0.536421359451 0.494231153629 0.521485741525 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0170416936310076 0.0 0.0 0.9966523437234583 0.0 0.0 0.243019993889 1.0 0.531042555263 1.0502162457 0.785910719907 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.5661470096033507 0.0 0.0 1.4866199159850062 0.0 0.0 0.509407808634 1.0 0.540314165243 0.392988931151 0.427891527473 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9005390922630547 0.0 0.0 0.9156116196100091 0.0 0.0 0.2582240867 1.0 0.535511969658 0.906408832365 0.946349515527 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 0.9005390922630547 0.0 0.0 0.9156116196100091 0.0 0.0 0.205903473831 1.0 0.573348389986 0.629137151574 0.889290089182 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.6900295423597387 0.0 0.0 1.4957258727081972 0.0 0.0 0.440750590721 1.0 0.504977648605 0.431890389444 0.390841236866 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.6900295423597387 0.0 0.0 1.4957258727081972 0.0 0.0 0.479572371464 1.0 0.534797031437 0.364124398349 0.446981565025 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.7488882101460559 0.0 0.0 1.6102024143738476 0.0 0.0 0.454681058285 1.0 0.573157026775 0.455035838586 0.530631741377 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.7488882101460559 0.0 0.0 1.6102024143738476 0.0 0.0 0.383400479855 1.0 0.518067644768 0.403598469277 0.530631741377 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.7960625450935717 0.0 0.0 1.633560126255662 0.0 0.0 0.512327170126 1.0 0.514063029844 0.403598469277 0.46432354738 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.8489898868913712 0.0 0.0 1.7131774325449236 0.0 0.0 0.416285905659 1.0 0.563403257533 0.328971986186 0.507099504549 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.8489898868913712 0.0 0.0 1.7131774325449236 0.0 0.0 0.441843724524 1.0 0.754283185969 0.391584742068 0.507099504549 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.6753782879542682 0.0 0.0 1.6043593119072894 0.0 0.0 0.447133248472 1.0 0.518674418144 0.291479062179 0.597236906585 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.8966912203660395 0.0 0.0 1.6972283177012926 0.0 0.0 0.455395353552 1.0 0.600371999086 0.261842471305 0.597236906585 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.7876943161436514 0.0 0.0 1.6066514643223342 0.0 0.0 0.481485228312 1.0 0.585382550141 0.253644626631 0.552908247334 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

15000 15000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.7501853246803238 0.0 0.0 1.5689896491416353 0.0 0.0 0.534278378363 1.0 0.56644058668 0.292953440194 0.481236414075 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.695830995474257 0.0 0.0 1.5413910448134973 0.0 0.0 0.534278378363 1.0 0.562019589429 0.292953440194 0.481236414075 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.695830995474257 0.0 0.0 1.5413910448134973 0.0 0.0 0.493175691565 1.0 0.507207236101 0.295699718183 0.540788285807 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.695830995474257 0.0 0.0 1.5413910448134973 0.0 0.0 0.552147704273 1.0 0.584839518361 0.291438866723 0.540788285807 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.7061203935094418 0.0 0.0 1.5376957052918416 0.0 0.0 0.552147704273 1.0 0.618738156193 0.291438866723 0.540788285807 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.7061203935094418 0.0 0.0 1.5376957052918416 0.0 0.0 0.552147704273 1.0 0.532580542543 0.291438866723 0.540788285807 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

15000 15000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.506423496356251 0.0 0.0 1.3902741780469345 0.0 0.0 0.521407890784 1.0 0.675029905771 0.268789298541 0.515451861363 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.6545963186823176 0.0 0.0 1.5112913709479174 0.0 0.0 0.502976082419 1.0 0.541188954876 0.399354928396 0.658179846106 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

15000 15000

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:1693: RuntimeWarning: invalid value encountered in double_scalars

for key in keys:

Iteration: 15000

Rhat values:

alphaN: 1.2938049011196477 nan nan

beta: 1.119807566434564 nan nan

sig0: 1.392605719937651

k: nan

ximean: 1.0335030397562068

xivar: 0.9999965394229083

pbad: 1.290689347565135

outlier_mean: 1.0800638243007186

outlier_sigma: 1.0653718728741803

_build_chain

In [2]: